Modern map data has come under increasing demands for rapid generalisation and scalability in the digital age. Where previously a cartographer would draw up a map to a particular scale based on the needs of the user—so that a small scale map would be drawn up for motor vehicle users travelling long distances, and a larger scale map would be created for those travelling on foot—modern users viewing maps on computing devices demand instant scalability of map data. These requirements can pose a number of problems.
Most providers of digital maps rely on a very detailed topographical map database which stores the underlying data. For example, Ordnance Survey uses the very large scale topographic product OS MasterMap®, which records every feature larger than a few meters in one continuous map, and is constantly being updated. Users viewing OS MasterMap® data on a digital device zoom in and out depending on their needs. Rather than draw each zoom level separately and dynamically it is more prudent to create different map images at different zoom levels from the underlying map data in advance. This means that any updates to the underlying data will need to be reproduced at all zoom levels. If a new road is constructed, for example, it would be very time consuming for a technician to have to update multiple zoom versions of the map with the same amendments manually. Raster coverage of Great Britain at 1:50000 was updated manually, and independently of the MasterMap data. OS VectorMap District was the first OS product to be derived automatically from large scale data.
If when creating map images at different zoom levels the underlying data were simply scaled, while retaining all of the data of the underlying map, at a larger scale the map would become unreadable due to the concentration of map features. Therefore generalisation of the features of the map is needed. Generalisation refers to representing concentrations of features as one single feature—for example the individual buildings making up a block in a city will be visible at a high zoom level, but will be viewed as a single entity on a larger scale version of the map.
One of the most widely used aspects of the topographical map data is that of the road network. As an example, the layer of the OS MasterMap which incorporates the topographical road network data is the Integrated Transport Network™ (ITN) layer. The OS model uses a link and node structure that is connected into a single network to depict the road infrastructure in Great Britain. In the context of producing topographic maps, generalisation of the road network is a major task, which requires significant processing of the underlying map data.
To maintain the fidelity of the network, the process of generalising features of the source data which makes up the ITN layer must not lead to the connectivity of roads, paths or junctions being lost. This is important when considering the representation of dual carriageways at an image zoom level where the carriageways are to be represented as one single line.
Dual carriageways (known in the US as divided highways), in which two carriageways separated by a central reservation (or median) are provided for traffic travelling in opposite directions, are represented by two lines in OS large scale road network data, with each line representing a given direction of one way carriageway. This is particularly the case with zoomed-in, large scale map images. For more zoomed-out map images, i.e. smaller scale, which represent a greater geographical area per unit image size, then in order to depict the road with the appropriate symbol, the two lines often need to be merged, or collapsed into a centre line.
A previous solution to this problem was given in the paper “Strategy for Collapsing OS Integrated Transport Network™ Dual Carriageways”, presented at 8th ICA Workshop on Generalisation and Multiple representation, A Coruña, July 2005 (hereafter the “2005 paper”). The aim of the technique described in the paper was to collapse a dual carriageway to a single centreline representing that dual carriageway. However, the described technique is computationally intensive, and the program was in fact non-scaleable and failed when applied to the entire map database. A less computationally intensive solution, and also one which can be applied only to a subset of map data is therefore desirable.